GOES-East and GOES-West satellite data are made available on Amazon Web Services through NOAA's Open Data Dissemination Program. GOES-2-go is a python package that makes it easy to find and download the files you want from AWS to your local computer with some additional helpers to visualize and understand the data.
The easiest way to install goes2go and its dependencies is with Conda from conda-forge.
conda install -c conda-forge goes2go
You may also create the provided Conda environment, environment.yml.
# Download environment file
wget https://github.com/blaylockbk/goes2go/raw/main/environment.yml
# Modify that file if you wish.
# Create the environment
conda env create -f environment.yml
# Activate the environment
conda activate goes2goAlternatively, goes2go is published on PyPI and you can install it with pip, but it requires some additional dependencies that you will have to install yourself:
- Python 3.8+
- Cartopy, which requires GEOS and Proj (if using
cartopy<0.22.0). - MetPy
- Optional: Carpenter Workshop
When those are installed within your environment, then you can install GOES-2-go with pip.
# Latest published version
pip install goes2go
# ~~ or ~~
# Most recent changes
pip install git+https://github.com/blaylockbk/goes2go.git graph TD;
aws16[(AWS\nnoaa-goes16)] -.-> G
aws17[(AWS\nnoaa-goes17)] -.-> G
aws18[(AWS\nnoaa-goes18)] -.-> G
G((. GOES 2-go .))
G --- .latest
G --- .nearesttime
G --- .timerange
.latest --> ds[(xarray.DataSet)]
.nearesttime --> ds[(xarray.DataSet)]
.timerange --> ds[(xarray.DataSet)]
ds --- rgb[ds.rgb\naccessor to make RGB composites]
ds --- fov[ds.FOV\naccessor to get field-of-view polygons]
style G fill:#F8AF22,stroke:#259DD7,stroke-width:4px,color:#000000
Download GOES ABI or GLM NetCDF files to your local computer. Files can also be read with xarray.
First, create a GOES object to specify the satellite, data product, and domain you are interested in. The example below downloads the Multi-Channel Cloud Moisture Imagery for CONUS.
from goes2go import GOES
# ABI Multi-Channel Cloud Moisture Imagry Product
G = GOES(satellite=16, product="ABI-L2-MCMIP", domain='C')
# Geostationary Lightning Mapper
G = GOES(satellite=17, product="GLM-L2-LCFA", domain='C')
# ABI Level 1b Data
G = GOES(satellite=17, product="ABI-L1b-Rad", domain='F')A complete listing of the products available are available here.
There are methods to do the following:
- List the available files for a time range
- Download data to your local drive for a specified time range
- Read the data into an xarray Dataset for a specific time
# Produce a pandas DataFrame of the available files in a time range
df = G.df(start='2022-07-04 01:00', end='2022-07-04 01:30') # Download and read the data as an xarray Dataset nearest a specific time
ds = G.nearesttime('2022-01-01') # Download and read the latest data as an xarray Dataset
ds = G.latest() # Download data for a specified time range
G.timerange(start='2022-06-01 00:00', end='2022-06-01 01:00')
# Download recent data for a specific interval
G.timerange(recent='30min')The rgb xarray accessor computes various RGB products from a GOES ABI ABI-L2-MCMIP (multi-channel cloud and moisture imagry products) xarray.Dataset. See the demo for more examples of RGB products.
import matplotlib.pyplot as plt
ds = GOES().latest()
ax = plt.subplot(projection=ds.rgb.crs)
ax.imshow(ds.rgb.TrueColor(), **ds.rgb.imshow_kwargs)
ax.coastlines()
The FOV xarray accessor creates shapely.Polygon objects for the ABI and GLM field of view. See notebooks for GLM and ABI field of view.
from goes2go.data import goes_latest
G = goes_latest()
# Get polygons of the full disk or ABI domain field of view.
G.FOV.full_disk
G.FOV.domain
# Get Cartopy coordinate reference system
G.FOV.crsGOES-West is centered over -137 W and GOES-East is centered over -75 W. When GOES was being tested, it was in a "central" position, outlined in the dashed black line. Below is the ABI field of view for the full disk:
The GLM field of view is slightly smaller and limited by a bounding box. Below is the approximated GLM field of view:
If GOES-2-go played an important role in your work, please tell me about it! Also, consider including a citation or acknowledgement in your article or product.
Suggested Citation
Blaylock, B. K. (2023). GOES-2-go: Download and display GOES-East and GOES-West data (Version 2022.07.15) [Computer software]. https://github.com/blaylockbk/goes2go
Suggested Acknowledgment
A portion of this work used code generously provided by Brian Blaylock's GOES-2-go python package (https://github.com/blaylockbk/goes2go)
As an alternative you can use rclone to download GOES files from AWS. I quite like rclone. Here is a short rclone tutorial.
I hope you find this makes GOES data easier to retrieve and display. Enjoy!
- Brian Blaylock
👨🏻💻 Contributing Guidelines
💬 GitHub Discussions
🚑 GitHub Issues
🌐 Personal Webpage
P.S. If you like GOES-2-go, check out my other python packages
- 🏁 Herbie: download numerical weather model data
- 🌡️ SynopticPy: retrieve mesonet data from the Synoptic API.
- 🌹 Pandas-rose: easly wind rose from Pandas dataframe.